Abstract
Ensuring the geometric accuracy of 3D printed parts has proven to be a significant challenge. A recent body of work has developed strategies for using statistical modeling and machine learning to learn from shape accuracy data of past prints to predict and compensate for errors in future prints. Unfortunately, this approach faces a number of challenges that make it difficult to translate from theory to industrial practice. This paper addresses these challenges by proposing a framework for a distributed system through which this modeling methodology could be deployed in an industrial setting. Further, a prototype of this system is illustrated.
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